Optimization for Gene Selection and Cancer Classification

Abstract

Recently, gene selection has played an important role in cancer diagnosis and classification. In this study, it was studied to select high descriptive genes for use in cancer diagnosis in order to develop a classification analysis for cancer diagnosis using microarray data. For this purpose, comparative analysis and intersections of six different methods obtained by using two feature selection algorithms and three search algorithms are presented. As a result of the six different feature subset selection methods applied, it was seen that instead of 15,155 genes, 24 genes should be focused. In this case, cancer diagnosis may be possible using 24 candidate genes that have been reduced, rather than similar studies involving larger features. However, in order to see the diagnostic success of diagnoses made using these candidate genes, they should be examined in a wet laboratory.